system

Abstract

An object of a system according to an embodiment is to automatically post an appropriate content to an SNS based on personal information of a user.SOLUTION: A system according to an embodiment includes a personal information collection unit, a content generation unit, and a proxy posting unit. The personal information collection unit collects personal information of a user. The content generation unit generates content on the basis of the personal information collected by the personal information collection unit. The proxy posting unit posts the content generated by the content generation unit to the SNS platform.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult to post appropriate content to SNS based on a user's personal information.

[0005] The system according to the embodiment aims to automatically post appropriate content to an SNS based on the user's personal information. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal information collection unit, a content generation unit, and a proxy posting unit. The personal information collection unit collects personal information of a user. The content generation unit generates content based on the personal information collected by the personal information collection unit. The proxy posting unit posts the content generated by the content generation unit to an SNS platform. [Effects of the Invention]

[0007] The system according to the embodiment can automatically post appropriate content to an SNS based on the user's personal information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The SNS personal support system according to an embodiment of the present invention is a system that automatically generates content based on a user's personal information and posts it on major SNS platforms on the user's behalf, thereby improving the user's presence on SNS.

[0029] The SNS personal support system according to the embodiment includes a personal information collection unit, a content generation unit, and a proxy posting unit. The personal information collection unit collects personal information about the user. For example, the personal information collection unit collects information about the user, such as the user's interests, past posts, and follower responses. The personal information collection unit also analyzes the user's past posts and "liked" posts to understand the user's preferences and interests. For example, if the user likes traveling, the personal information collection unit collects information for generating posts containing travel information and photos. The content generation unit generates content based on the collected personal information. For example, the generation AI generates posts containing travel information and photos based on prompts reflecting the user's interests. If the user is interested in a particular sport, the generation AI generates posts containing news and event information related to that sport. The proxy posting unit posts the generated content to an SNS platform. For example, the generated content may be posted to X or shared on a LINE timeline. It may also be posted to a Facebook feed. This allows the SNS personal support system to enhance the user's presence on SNS. For example, even in busy daily lives, users can post high-quality content regularly, maintaining engagement with their followers. In addition, by generating content that reflects the user's personality and preferences, users will receive a positive response from their followers, increasing their influence on social media.

[0030] The personal information collection unit collects real-time behavioral data of the user and can analyze more detailed personal information. The personal information collection unit, for example, collects GPS data from the user's smartphone and analyzes daily movement patterns. For example, the user's interests and lifestyle can be understood based on the places the user frequently visits and the travel time. The personal information collection unit also collects app usage history and analyzes the user's interests and behavioral patterns. For example, the user's interests and concerns can be understood based on the apps the user frequently uses and how often they are used. In this way, by collecting real-time behavioral data of the user and analyzing more detailed personal information, more accurate content can be generated.

[0031] The personal information collection unit can share a user's personal information across different SNS platforms and perform integrated analysis. The personal information collection unit, for example, links a user's SNS accounts, such as X, LINE, and Facebook, and integrates the personal information collected from each platform. For example, it centrally manages the content of posts and "like" history on each platform. The personal information collection unit also automates data sharing across different SNS platforms and builds a system for integrated analysis. For example, it collects data from each platform in real time and analyzes it in an integrated manner. This allows personal information to be shared across different SNS platforms and integrated analysis can be performed to obtain more detailed personal information.

[0032] The personal information collection unit can link the user's personal information with other digital services to collect more multifaceted information. The personal information collection unit, for example, collects the user's music streaming service history and analyzes the user's music preferences. For example, the personal information collection unit updates the personal information based on the artists and genres the user frequently listens to. The personal information collection unit also collects the user's e-book service history and analyzes the user's reading preferences. For example, the personal information is updated based on the genres and authors the user frequently reads. In this way, by linking with other digital services, more multifaceted personal information can be collected.

[0033] The content generation unit analyzes the content posted by the user in the past and extracts posting patterns and trends, thereby generating more accurate content. The content generation unit, for example, analyzes the content posted by the user in the past and identifies the frequency and time period of posting. For example, it grasps the tendency of users to post on specific days of the week or at specific time periods, and generates content that matches those time periods. The content generation unit also analyzes the content posted by the user in the past and extracts trends in the content and themes of posts. For example, it generates the content of the next post based on themes and topics that the user often posts about. In this way, by analyzing the content posted by the user in the past and extracting posting patterns and trends, more accurate content can be generated.

[0034] The content generation unit can analyze the reaction data of the user's followers and generate content that the followers will be most interested in. For example, the content generation unit analyzes posts that the user's followers have previously liked or commented on, and identifies the interests of the followers. For example, the content generation unit generates content based on themes or topics that followers respond well to. The content generation unit also analyzes the reaction data of followers in real time, and builds a system that generates content based on that data. For example, posts are generated based on the time periods that followers are most interested in. In this way, engagement can be increased by analyzing the reaction data of followers and generating content that interests them most.

[0035] The content generation unit can automatically generate content in different languages ​​and accommodate international followers. The content generation unit, for example, builds a system that automatically generates content in different languages ​​based on a user's personal information. For example, it supports multiple languages ​​such as English, French, and Chinese. The content generation unit also automates the generation of content in different languages ​​and builds a system that accommodates international followers. For example, it automatically translates user posts and posts them in each language. This allows the system to automatically generate content in different languages ​​and accommodate international followers, thereby increasing global engagement.

[0036] The content generation unit generates content in different genres based on the user's hobbies and interests, allowing the user to post a variety of content. The content generation unit, for example, analyzes the user's hobbies and interests and generates content in different genres. For example, if the user is interested in cooking and traveling, the content generation unit generates posts including cooking recipes and travelogues. The content generation unit also builds a system that generates content in different genres, such as sports, entertainment, and news, based on the user's hobbies and interests. For example, if the user is interested in sports, the content generation unit generates posts including the latest sports news and game results. In this way, the user's presence on the SNS can be improved by generating content in different genres based on the user's hobbies and interests and posting a variety of content.

[0037] The proxy posting unit can analyze the active time periods of followers and post according to those time periods in order to optimize the timing of posts. The proxy posting unit, for example, analyzes the active time periods of followers and builds a system that posts according to those time periods. For example, it automatically schedules posts for the time periods when followers are most active. The proxy posting unit also builds a system that analyzes the active time periods of followers in real time and optimizes the timing of posts based on that data. For example, it posts according to the time periods when followers are most likely to respond. In this way, by posting according to the active time periods of followers, it is possible to increase engagement.

[0038] The proxy posting unit can automatically generate posts in different formats to increase the variety of post content. The proxy posting unit, for example, builds a system that automatically generates posts in different formats based on a user's personal information. For example, it generates posts in image, video, and text formats. The proxy posting unit also automates posting in different formats to build a system that increases the variety of post content. For example, it generates a user's post content in image, video, and text formats and posts them to a social media platform. In this way, by automatically generating posts in different formats, it is possible to increase the variety of post content and increase engagement.

[0039] The proxy posting unit automates cross-posting between different SNS platforms, enabling consistent posting across multiple platforms. The proxy posting unit builds a system that automates cross-posting between different SNS platforms, for example, based on a user's personal information. For example, it posts simultaneously to X, LINE, and Facebook. The proxy posting unit also automates cross-posting between different SNS platforms, building a system that enables consistent posting across multiple platforms. For example, it posts the same content to multiple platforms simultaneously. This allows consistent posting across multiple platforms by automating cross-posting between different SNS platforms.

[0040] The proxy posting unit can select the most effective posting format based on the user's past posting data and post in that format. The proxy posting unit, for example, analyzes the user's past posting data and builds a system that selects the most effective posting format. For example, it compares the effectiveness of image, video, and text formats and selects the optimal format. The proxy posting unit also builds a system that selects the most effective posting format based on the user's past posting data and posts in that format. For example, it compares the effectiveness of posts with images, video posts, and text-only posts and selects the optimal format. In this way, by selecting the most effective posting format based on the user's past posting data and posting in that format, engagement can be increased.

[0041] The system can collect user feedback in real time and immediately reflect it in the next content generation. For example, the system can be configured to collect user feedback in real time and reflect that data in the next content generation. For example, the system can analyze the content of users' "likes" and comments and reflect that in the next post. The system can also be configured to collect user feedback in real time and immediately reflect that data. For example, the system can adjust the content of the next post based on the user feedback. In this way, by collecting user feedback in real time and immediately reflecting it in the next content generation, it is possible to provide content that is more suited to the user.

[0042] The system can analyze the content of the feedback in detail, extract specific areas for improvement, and reflect them in the next post. For example, the system builds a system that analyzes the content of the user's feedback in detail and extracts specific areas for improvement. For example, the next post is improved based on the problems pointed out by the user. The system also builds a system that analyzes the content of the feedback in detail and extracts specific areas for improvement based on that data. For example, the content of the feedback is classified and frequency analysis is performed to identify common areas for improvement. This makes it possible to analyze the content of the feedback in detail, extract specific areas for improvement, and reflect them in the next post, thereby providing content that is more suited to the user.

[0043] The system can share feedback across different SNS platforms and make integrated improvements. For example, the system builds a system that shares user feedback across different SNS platforms and makes integrated improvements. For example, it centrally manages feedback from X, LINE, and Facebook. The system also builds a system that automates the sharing of feedback across different SNS platforms and makes integrated improvements. For example, it collects feedback from each platform in real time and analyzes it in an integrated manner. This allows feedback to be shared across different SNS platforms and makes integrated improvements, making it possible to provide content that is more suited to users.

[0044] The system can link user feedback with other digital services to make comprehensive improvements. For example, the system can link user feedback with customer support and product reviews to build a system that makes comprehensive improvements. For example, the next post can be improved based on customer support feedback. The system can also link user feedback with other digital services to build a system that makes comprehensive improvements. For example, the next post can be improved based on product review feedback. In this way, by linking feedback with other digital services and making comprehensive improvements, it is possible to provide content that is more suited to users.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The SNS personal support system can also collect user health data and generate content based on the user's health status. For example, it can collect heart rate and sleep data from the user's smartwatch and generate posts containing health advice and reminders. It can also analyze data from the user's food recording app and generate posts that provide information on nutritional balance. This allows it to generate content based on the user's health status and increase health awareness.

[0047] The SNS personal support system can also generate content based on the user's hobbies and skills. For example, if the user's hobby is music, it can generate posts about the latest music news and how to play instruments. If the user's hobby is cooking, it can generate posts introducing new recipes and cooking tips. This allows the system to generate content based on the user's hobbies and skills, and draw out common interests with followers.

[0048] The SNS personal support system can also reflect user feedback in the next content generation. For example, if a user gives many likes and comments on a particular post, the system can reflect that theme and style in the next post. Also, if a user gives negative feedback on a particular post, the system can improve the content and reflect it in the next post. This allows the system to improve content based on user feedback and provide content that is more suited to the user.

[0049] The SNS personal support system can also use data on the reaction of a user's followers to reflect the next content generation. For example, if followers give many likes and comments to a particular post, the system can reflect that theme or style in the next post. Also, if followers have a negative reaction to a particular post, the system can improve the content and reflect that in the next post. This allows the system to improve content based on follower reaction data and provide content that is more engaging.

[0050] The SNS personal support system can also optimize the content of a user's next post based on the user's past posting data. For example, the theme or style that received the most positive response among the user's past posts can be reflected in the next post. The system can also optimize the content of the next post by avoiding themes or styles that received the least positive response among the user's past posts. This allows the system to optimize the content of the next post based on the user's past posting data, making it possible to provide content that will attract more engagement.

[0051] The SNS personal support system can also automatically generate content in different languages ​​based on the user's personal information. For example, if a user speaks multiple languages, it can generate content corresponding to those languages. Also, if the user's followers speak different languages, it can generate content corresponding to those languages. This allows for the automatic generation of content in different languages ​​and supports international followers, thereby increasing global engagement.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The personal information collection unit collects personal information about the user. For example, it collects information about the user's interests, past posts, and followers' reactions. It also analyzes the content the user has posted in the past and the posts they have "liked" to understand the user's preferences and interests. For example, if the user likes traveling, it collects information to generate posts that include travel information and photos. Step 2: The content generator generates content based on the collected personal information. For example, the AI ​​generates posts containing travel information and photos based on prompts that reflect the user's interests. If the user is interested in a particular sport, it generates posts containing news and event information related to that sport. Step 3: The proxy posting unit posts the generated content to a social media platform. For example, the content generated by the generation AI can be posted to X or shared on the LINE timeline. It can also be posted to a Facebook feed. This can increase the user's presence on social media.

[0054] (Example 2) The SNS personal support system according to an embodiment of the present invention is a system that automatically generates content based on a user's personal information and posts it on major SNS platforms on the user's behalf, thereby improving the user's presence on SNS.

[0055] The SNS personal support system according to the embodiment includes a personal information collection unit, a content generation unit, and a proxy posting unit. The personal information collection unit collects personal information about the user. For example, the personal information collection unit collects information about the user, such as the user's interests, past posts, and follower responses. The personal information collection unit also analyzes the user's past posts and "liked" posts to understand the user's preferences and interests. For example, if the user likes traveling, the personal information collection unit collects information for generating posts containing travel information and photos. The content generation unit generates content based on the collected personal information. For example, the generation AI generates posts containing travel information and photos based on prompts reflecting the user's interests. If the user is interested in a particular sport, the generation AI generates posts containing news and event information related to that sport. The proxy posting unit posts the generated content to an SNS platform. For example, the generated content may be posted to X or shared on a LINE timeline. It may also be posted to a Facebook feed. This allows the SNS personal support system to enhance the user's presence on SNS. For example, even in busy daily lives, users can post high-quality content regularly, maintaining engagement with their followers. In addition, by generating content that reflects the user's personality and preferences, users will receive a positive response from their followers, increasing their influence on social media.

[0056] The personal information collection unit collects real-time behavioral data of the user and can analyze more detailed personal information. The personal information collection unit, for example, collects GPS data from the user's smartphone and analyzes daily movement patterns. For example, the user's interests and lifestyle can be understood based on the places the user frequently visits and the travel time. The personal information collection unit also collects app usage history and analyzes the user's interests and behavioral patterns. For example, the user's interests and concerns can be understood based on the apps the user frequently uses and how often they are used. In this way, by collecting real-time behavioral data of the user and analyzing more detailed personal information, more accurate content can be generated.

[0057] The personal information collection unit can use the emotion estimation function to analyze the emotional state at the time of posting and collect personal information based on the emotion. For example, the personal information collection unit captures the user's facial expression when posting with a camera and analyzes the emotional state using an emotion estimation algorithm. For example, it detects smiling or surprised expressions and reflects those emotions in the personal information. The personal information collection unit also collects voice data when the user posts and estimates the emotion using voice analysis technology. For example, it analyzes the tone and speed of the voice and calculates an emotion score. In this way, by analyzing the user's emotional state and collecting personal information based on the emotion, it is possible to generate content that is more tailored to the user.

[0058] The personal information collection unit can analyze the user's voice data and reflect the emotion and tone obtained from the voice in the personal information. The personal information collection unit, for example, collects voice data when the user posts and analyzes the emotion and tone using voice analysis technology. For example, the emotion is estimated based on the pitch and strength of the voice and this information is reflected in the personal information. The personal information collection unit also analyzes the user's voice data in real time and builds a system that instantly reflects the emotion and tone. For example, if the user is in a positive emotional state, content that reflects that emotion is generated. In this way, by analyzing the user's voice data and reflecting the emotion and tone in the personal information, content that is more suited to the user can be generated.

[0059] The personal information collection unit can share a user's personal information across different SNS platforms and perform integrated analysis. The personal information collection unit, for example, links a user's SNS accounts, such as X, LINE, and Facebook, and integrates the personal information collected from each platform. For example, it centrally manages the content of posts and "like" history on each platform. The personal information collection unit also automates data sharing across different SNS platforms and builds a system for integrated analysis. For example, it collects data from each platform in real time and analyzes it in an integrated manner. This allows personal information to be shared across different SNS platforms and integrated analysis can be performed to obtain more detailed personal information.

[0060] The personal information collection unit can link the user's personal information with other digital services to collect more multifaceted information. The personal information collection unit, for example, collects the user's music streaming service history and analyzes the user's music preferences. For example, the personal information collection unit updates the personal information based on the artists and genres the user frequently listens to. The personal information collection unit also collects the user's e-book service history and analyzes the user's reading preferences. For example, the personal information is updated based on the genres and authors the user frequently reads. In this way, by linking with other digital services, more multifaceted personal information can be collected.

[0061] The personal information collection unit can use the emotion estimation function to analyze the emotions a user has toward specific content and collect personal information based on those emotions. The personal information collection unit, for example, analyzes the user's emotional response to a video they are watching and reflects the emotion data in the personal information. For example, it analyzes facial expressions and voice while watching the video and calculates an emotion score. The personal information collection unit also analyzes the user's emotional response to an article they are viewing and reflects the emotion data in the personal information. For example, it analyzes facial expressions and voice while viewing the article and calculates an emotion score. In this way, by using the emotion estimation function to analyze the emotions a user has toward specific content and collecting personal information based on those emotions, it is possible to generate content that is more tailored to the user.

[0062] The content generation unit analyzes the content posted by the user in the past and extracts posting patterns and trends, thereby generating more accurate content. The content generation unit, for example, analyzes the content posted by the user in the past and identifies the frequency and time period of posting. For example, it grasps the tendency of users to post on specific days of the week or at specific time periods, and generates content that matches those time periods. The content generation unit also analyzes the content posted by the user in the past and extracts trends in the content and themes of posts. For example, it generates the content of the next post based on themes and topics that the user often posts about. In this way, by analyzing the content posted by the user in the past and extracting posting patterns and trends, more accurate content can be generated.

[0063] The content generation unit uses the emotion estimation function to generate content based on emotions and post content that matches the user's emotional state. The content generation unit, for example, analyzes the user's emotional state in real time and generates content based on that emotion. For example, if the user is in a positive emotional state, it generates a post with a bright tone. The content generation unit also builds a system that analyzes the user's emotional state and generates content based on that emotion. For example, if the user is in a negative emotional state, it generates a post that includes an encouraging message. In this way, content that matches the user's emotional state can be generated and posted, making it possible to provide content that is more suited to the user.

[0064] The content generation unit can analyze the reaction data of the user's followers and generate content that the followers will be most interested in. For example, the content generation unit analyzes posts that the user's followers have previously liked or commented on, and identifies the interests of the followers. For example, the content generation unit generates content based on themes or topics that followers respond well to. The content generation unit also analyzes the reaction data of followers in real time, and builds a system that generates content based on that data. For example, posts are generated based on the time periods that followers are most interested in. In this way, engagement can be increased by analyzing the reaction data of followers and generating content that interests them most.

[0065] The content generation unit can automatically generate content in different languages ​​and accommodate international followers. The content generation unit, for example, builds a system that automatically generates content in different languages ​​based on a user's personal information. For example, it supports multiple languages ​​such as English, French, and Chinese. The content generation unit also automates the generation of content in different languages ​​and builds a system that accommodates international followers. For example, it automatically translates user posts and posts them in each language. This allows the system to automatically generate content in different languages ​​and accommodate international followers, thereby increasing global engagement.

[0066] The content generation unit generates content in different genres based on the user's hobbies and interests, allowing the user to post a variety of content. The content generation unit, for example, analyzes the user's hobbies and interests and generates content in different genres. For example, if the user is interested in cooking and traveling, the content generation unit generates posts including cooking recipes and travelogues. The content generation unit also builds a system that generates content in different genres, such as sports, entertainment, and news, based on the user's hobbies and interests. For example, if the user is interested in sports, the content generation unit generates posts including the latest sports news and game results. In this way, the user's presence on the SNS can be improved by generating content in different genres based on the user's hobbies and interests and posting a variety of content.

[0067] The content generation unit uses the emotion estimation function to generate content that evokes the most positive emotions in users, thereby increasing engagement. The content generation unit generates content that evokes the most positive emotions, for example, based on the user's emotion estimation data. For example, it generates humorous posts that make users smile. The content generation unit also analyzes the user's emotion estimation data in real time and builds a system that generates content that elicits positive emotions based on that data. For example, it generates posts that include stories and images that move the user. This makes it possible to increase engagement by generating content that evokes the most positive emotions in users.

[0068] The proxy posting unit can analyze the active time periods of followers and post according to those time periods in order to optimize the timing of posts. The proxy posting unit, for example, analyzes the active time periods of followers and builds a system that posts according to those time periods. For example, it automatically schedules posts for the time periods when followers are most active. The proxy posting unit also builds a system that analyzes the active time periods of followers in real time and optimizes the timing of posts based on that data. For example, it posts according to the time periods when followers are most likely to respond. In this way, by posting according to the active time periods of followers, it is possible to increase engagement.

[0069] The proxy posting unit can use the user's emotion estimation function to select the optimal posting timing based on the user's emotion. The proxy posting unit, for example, builds a system that analyzes the user's emotional state in real time and selects the optimal posting timing based on that emotion. For example, if the user is in a positive emotional state, the proxy posting unit posts at that timing. The proxy posting unit also builds a system that selects the optimal posting timing based on the user's emotion based on the user's emotion estimation data. For example, the proxy posting unit posts at the time when the user is most relaxed. This allows engagement to be increased by selecting the optimal posting timing based on the user's emotion.

[0070] The proxy posting unit can automatically generate posts in different formats to increase the variety of post content. The proxy posting unit, for example, builds a system that automatically generates posts in different formats based on a user's personal information. For example, it generates posts in image, video, and text formats. The proxy posting unit also automates posting in different formats to build a system that increases the variety of post content. For example, it generates a user's post content in image, video, and text formats and posts them to a social media platform. In this way, by automatically generating posts in different formats, it is possible to increase the variety of post content and increase engagement.

[0071] The proxy posting unit automates cross-posting between different SNS platforms, enabling consistent posting across multiple platforms. The proxy posting unit builds a system that automates cross-posting between different SNS platforms, for example, based on a user's personal information. For example, it posts simultaneously to X, LINE, and Facebook. The proxy posting unit also automates cross-posting between different SNS platforms, building a system that enables consistent posting across multiple platforms. For example, it posts the same content to multiple platforms simultaneously. This allows consistent posting across multiple platforms by automating cross-posting between different SNS platforms.

[0072] The proxy posting unit can select the most effective posting format based on the user's past posting data and post in that format. The proxy posting unit, for example, analyzes the user's past posting data and builds a system that selects the most effective posting format. For example, it compares the effectiveness of image, video, and text formats and selects the optimal format. The proxy posting unit also builds a system that selects the most effective posting format based on the user's past posting data and posts in that format. For example, it compares the effectiveness of posts with images, video posts, and text-only posts and selects the optimal format. In this way, by selecting the most effective posting format based on the user's past posting data and posting in that format, engagement can be increased.

[0073] The proxy posting unit can use the emotion estimation function to predict the emotional reactions of followers and adjust the content of posts based on those reactions. The proxy posting unit, for example, builds a system that predicts the emotional reactions of followers and adjusts the content of posts based on those reactions. For example, it prioritizes posting content that is predicted to elicit a positive reaction. The proxy posting unit also builds a system that analyzes the emotional reactions of followers in real time and adjusts the content of posts based on that data. For example, it generates posts based on content that followers are most likely to respond to. This makes it possible to increase engagement by predicting the emotional reactions of followers and adjusting the content of posts based on those reactions.

[0074] The system can collect user feedback in real time and immediately reflect it in the next content generation. For example, the system can be configured to collect user feedback in real time and reflect that data in the next content generation. For example, the system can analyze the content of users' "likes" and comments and reflect that in the next post. The system can also be configured to collect user feedback in real time and immediately reflect that data. For example, the system can adjust the content of the next post based on the user feedback. In this way, by collecting user feedback in real time and immediately reflecting it in the next content generation, it is possible to provide content that is more suited to the user.

[0075] The system can use the emotion estimation function to analyze the emotional state at the time of feedback and adjust the content of the next post based on that emotion. For example, the system builds a system that analyzes the emotional state of the user at the time of feedback and adjusts the content of the next post based on that emotion. For example, the system generates the next post based on feedback in a positive emotional state. The system also builds a system that analyzes the emotional state of the user at the time of feedback in real time and adjusts the content of the next post based on that data. For example, the system improves the next post based on feedback in a negative emotional state. In this way, by analyzing the emotional state at the time of feedback and adjusting the content of the next post based on that emotion, it is possible to provide content that is more suited to the user.

[0076] The system can analyze the content of the feedback in detail, extract specific areas for improvement, and reflect them in the next post. For example, the system builds a system that analyzes the content of the user's feedback in detail and extracts specific areas for improvement. For example, the next post is improved based on the problems pointed out by the user. The system also builds a system that analyzes the content of the feedback in detail and extracts specific areas for improvement based on that data. For example, the content of the feedback is classified and frequency analysis is performed to identify common areas for improvement. This makes it possible to analyze the content of the feedback in detail, extract specific areas for improvement, and reflect them in the next post, thereby providing content that is more suited to the user.

[0077] The system can share feedback across different SNS platforms and make integrated improvements. For example, the system builds a system that shares user feedback across different SNS platforms and makes integrated improvements. For example, it centrally manages feedback from X, LINE, and Facebook. The system also builds a system that automates the sharing of feedback across different SNS platforms and makes integrated improvements. For example, it collects feedback from each platform in real time and analyzes it in an integrated manner. This allows feedback to be shared across different SNS platforms and makes integrated improvements, making it possible to provide content that is more suited to users.

[0078] The system can link user feedback with other digital services to make comprehensive improvements. For example, the system can link user feedback with customer support and product reviews to build a system that makes comprehensive improvements. For example, the next post can be improved based on customer support feedback. The system can also link user feedback with other digital services to build a system that makes comprehensive improvements. For example, the next post can be improved based on product review feedback. In this way, by linking feedback with other digital services and making comprehensive improvements, it is possible to provide content that is more suited to users.

[0079] The system can use the emotion estimation function to analyze the emotional response to feedback and optimize the content of the next post based on that response. For example, the system analyzes the user's emotional response to feedback and builds a system that reflects that data in the content of the next post. For example, the system generates the next post based on feedback with a high percentage of positive emotional responses. The system also builds a system that analyzes the emotional response to feedback in real time and optimizes the content of the next post based on that data. For example, the system adjusts the next post based on feedback with a low percentage of negative emotional responses. In this way, by analyzing the emotional response to feedback and optimizing the content of the next post based on that response, it is possible to provide content that is more suited to the user.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The SNS personal support system can also collect user health data and generate content based on the user's health status. For example, it can collect heart rate and sleep data from the user's smartwatch and generate posts containing health advice and reminders. It can also analyze data from the user's food recording app and generate posts that provide information on nutritional balance. This allows it to generate content based on the user's health status and increase health awareness.

[0082] The SNS personal support system can also generate content based on the user's hobbies and skills. For example, if the user's hobby is music, it can generate posts about the latest music news and how to play instruments. If the user's hobby is cooking, it can generate posts introducing new recipes and cooking tips. This allows the system to generate content based on the user's hobbies and skills, and draw out common interests with followers.

[0083] The SNS personal support system can also use the user's emotion estimation function to provide relaxing content when the user is feeling stressed. For example, if the user is feeling stressed, it can generate posts containing relaxing music or meditation guides. If the user is feeling anxious, it can generate posts containing encouraging messages or positive stories. This allows the system to provide relaxing content tailored to the user's emotional state and support stress reduction.

[0084] The SNS personal support system can also use the user's emotion estimation function to generate content for sharing a particular emotion when the user feels that emotion. For example, if the user is feeling happy, the system can generate a post containing positive messages and images to share that joy. Also, if the user is feeling sad, the system can generate a post containing empathetic stories and messages to make it easier for the user to share that emotion. This allows the system to provide content that makes it easier for users to share their emotions and strengthens emotional connections with their followers.

[0085] The SNS personal support system can also use the user's emotion estimation function to generate content to ease a user's emotions when they feel a certain way. For example, if a user is feeling angry, it can generate a post containing relaxation techniques or deep breathing guides to alleviate the anger. Also, if a user is feeling sad, it can generate a post containing encouraging messages or positive stories to ease the emotion. In this way, it can provide content that eases the user's emotions and support them in maintaining emotional balance.

[0086] The SNS personal support system can also reflect user feedback in the next content generation. For example, if a user gives many likes and comments on a particular post, the system can reflect that theme and style in the next post. Also, if a user gives negative feedback on a particular post, the system can improve the content and reflect it in the next post. This allows the system to improve content based on user feedback and provide content that is more suited to the user.

[0087] The SNS personal support system can also use data on the reaction of a user's followers to reflect the next content generation. For example, if followers give many likes and comments to a particular post, the system can reflect that theme or style in the next post. Also, if followers have a negative reaction to a particular post, the system can improve the content and reflect that in the next post. This allows the system to improve content based on follower reaction data and provide content that is more engaging.

[0088] The SNS personal support system can also optimize the content of a user's next post based on the user's past posting data. For example, the theme or style that received the most positive response among the user's past posts can be reflected in the next post. The system can also optimize the content of the next post by avoiding themes or styles that received the least positive response among the user's past posts. This allows the system to optimize the content of the next post based on the user's past posting data, making it possible to provide content that will attract more engagement.

[0089] The SNS personal support system can also use the user's emotion estimation function to generate content that will evoke the most positive emotions in users. For example, it can generate humorous posts that will make users smile, or posts that contain moving stories. It can also generate posts that contain natural scenes or soothing music that will help users relax. This allows the system to provide users with content that will evoke the most positive emotions, thereby increasing engagement.

[0090] The SNS personal support system can also automatically generate content in different languages ​​based on the user's personal information. For example, if a user speaks multiple languages, it can generate content corresponding to those languages. Also, if the user's followers speak different languages, it can generate content corresponding to those languages. This allows for the automatic generation of content in different languages ​​and supports international followers, thereby increasing global engagement.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The personal information collection unit collects personal information about the user. For example, it collects information about the user's interests, past posts, and followers' reactions. It also analyzes the content the user has posted in the past and the posts they have "liked" to understand the user's preferences and interests. For example, if the user likes traveling, it collects information to generate posts that include travel information and photos. Step 2: The content generator generates content based on the collected personal information. For example, the AI ​​generates posts containing travel information and photos based on prompts that reflect the user's interests. If the user is interested in a particular sport, it generates posts containing news and event information related to that sport. Step 3: The proxy posting unit posts the generated content to a social media platform. For example, the content generated by the generation AI can be posted to X or shared on the LINE timeline. It can also be posted to a Facebook feed. This can increase the user's presence on social media.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a personal information collection unit that collects personal information of users; a content generation unit that generates content based on the personal information collected by the personal information collection unit; a proxy posting unit that posts the content generated by the content generation unit to an SNS platform. A system characterized by:

2. The personal information collection unit The user's personal information is shared across different SNS platforms and analyzed comprehensively.

2. The system of claim 1.

3. The content generation unit Analyze the user's past posts, extract posting patterns and trends, and generate more accurate content.

2. The system of claim 1.

4. The proxy posting unit: Analyze the active times of your followers and post accordingly 2. The system of claim 1.

5. The personal information collection unit Analyzing the emotional state of the user at the time of posting and collecting the personal information based on the emotion.

2. The system of claim 1.

6. The content generation unit Generate content based on the user's emotions and post content that matches the user's emotional state.

2. The system of claim 1.

7. The proxy posting unit: Select the optimal posting timing based on the user's emotions 2. The system of claim 1.

8. The system comprises: Analyzing the emotional state of the user at the time of feedback and adjusting the content of the next post based on that emotion 2. The system of claim 1.

Images